Abstract:With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. How to allocate water resources optimally, rapidly, and effectively has become a challenging problem. Thus, this study employs a meta-heuristic swarm-based algorithm, the whale optimization algorithm (WOA). To overcome drawbacks like relatively low convergence precision and convergence rates, when applying the WOA algorithm to complex optimization problems, logistic mapping is used to initialize swarm location, and inertia weighting is employed to improve the algorithm. The resulting ameliorative whale optimization algorithm (AWOA) shows substantially enhanced convergence rates and precision than the WOA and particle swarm optimization algorithms, demonstrating relatively high reliability and applicability. A water resource allocation optimization model with optimal economic efficiency and least total water shortage volume is established for Handan, China, and solved by the AWOA. The allocation results better reflect actual water usage in Handan. In 2030, the p = 50% total water shortage is forecast as 404.34 × 10 6 m 3 or 14.8%. The shortage is mainly in the primary agricultural sector. The allocation results provide a reference for regional water resources management.

Droughts often have a substantial impact on normal socio-economic activities and agricultural production. The Haihe River Basin, one of the primary food production areas in China, has become increasingly sensitive to alternating droughts and floods, and the sharp transitions between them, due to rapid economic development and population growth combined with climate change. In this study, we employ the self-organizing map (SOM) neural network method to perform a cluster analysis on 43 meteorological stations in the study area, dividing the basin into five sub-regions. Then daily precipitation data are collected, and the number of continuous dry days is used as a drought index to investigate drought evolution trends. Lastly, the Pearson-III curve is used to analyze the first daily precipitation after different drought duration, and the relationships between precipitation intensity, drought duration, and interdecadal drought frequency are observed. The results demonstrate that under the climate warming of the Haihe River Basin, the frequency of droughts increases throughout the whole basin, while the droughts are of shorter duration, the probability of more intense first daily precipitation after droughts increases during the dry-wet transition. The research provides a useful reference for the planning and management of water resources in the Haihe River Basin.

The impact of global climate change on the temporal and spatial variations of precipitation is significant. In this study, daily temperature and precipitation data from 258 meteorological stations in the Haihe River Basin, for the period 1960–2020, were used to determine the trend and significance of temperature and precipitation changes at interannual and interseasonal scales. The Mann–Kendall test and Spearman’s correlation analysis were employed, and significant change trends and correlations were determined. At more than 90% of the selected stations, the results showed a significant increase in temperature, at both interannual and interseasonal scales, and the increasing trend was more significant in spring than in other seasons. Precipitation predominantly showed a decreasing trend at an interannual scale; however, the change trend was not significant. In terms of the interseasonal scale, the precipitation changes in spring and autumn showed an overall increasing trend, those in summer showed a 1:1 distribution ratio of increasing and decreasing trends, and those in winter showed an overall decreasing trend. Furthermore, the Spearman’s correlation analysis showed a negative correlation between temperature and precipitation in the entire Haihe River Basin, at both interannual and interseasonal scales; however, most of the correlations were weak.

Abstract. Watershed eco-compensation is an effective way to solve conflicts over water allocation and ecological destruction problems in the exploitation of water resources. Despite an increasing interest in the topic, the researches has neglected the effect of water quality and lacked systematic calculation method. In this study we reviewed and analyzed the current literature and proposedatheoretical framework to improve the calculation of co-compensation standard.Considering the perspectives of the river ecosystems, forest ecosystems and wetland ecosystems, the benefit compensation standard was determined by the input-output corresponding relationship. Based on the opportunity costs related to limiting development and water conservation loss, the eco-compensation standard was calculated.In order to eliminate the defects of eco-compensation implementation, the improvement suggestions were proposed for the compensation standard calculation and implementation. IntroductionWith the excessive exploitation and utilization of ecosystem services and excessive pollutant discharge, watershed eco-compensation (WEC), as an economic way of environment external effect internalization and an economic promotion method for ecosystem management, obtained widely attention. Since 1970s, the eco-compensation decision process and value impact assessment had been studied in many countries. A large number of compensation study cases of ecological environmental services have been reported, but the study scope is mostly limited to the evaluation of watershed ecological services, and the calculation methods for watershed eco-compensation standards had regional characteristics.Watershed eco-compensation standards (WECS) affect the feasibility of compensation mechanism. The essence of WECS is to determine the compensation amount, which can reflect ecological service values and environment protection input costs. Accurate calculation of water resource value is the prerequisite to determine the eco-compensation standard of the river basin. Xu and Han calculated related values of water resources with economic methods in different perspectives and levels and deduced the upper and lower limits of eco-compensation standards. Some scholars proposed new ideas for calculating the compensation standard based on the integrity of water quantity and quality, solving the defects of current economic calculation methods. The Game model by Cao, the eco-compensation standard calculation method based on water environmental capacity by Pang, and the ecocompensation standard calculation method based on water quality and total pollutant amount by Lu revealed the potential relationship between water quality and eco-compensation standards.

Land use affects regional hydrological processes. The alteration of regional distributions of vegetation, crop types, and land-use patterns for construction has a significant impact on the runoff process and influences the water cycle in watersheds. Studies on runoff variations in the Hutuo River Basin have concentrated on climate change and the effect of human activities without adequate attention paid to land-use changes. In order to investigate the response of runoff to land-use changes in the upper Hutuo River Basin, a soil and water assessment model was used in this study to compare and analyze the changes in runoff under five land-use scenarios from 1980–2020. The results show that the area of farmland, forest land, and grassland in the watershed gradually decreased from 1980 to 2020, with a total decrease of 3.1%, while the area of urban construction land increased rapidly by 1.5 times. Corresponding with the trend of land-use change, the differences between the simulated and natural values for regional flood peak and annual runoff increased with time, which is in line with the changing land-use trends. From 1960–2020, the differences between the simulated and natural values for the flood peaks of the five land-use scenarios were −16.8, −6.7, −3.5, 4.6, and 9.3%, respectively, and the errors between the simulated and natural values for annual runoff were −6.7, −4.4, −2.0, −2.6, and 10.8%, respectively. Overall, the increase in urban construction land and decrease in farming, forest area, and grassland has caused the regional flood peak and annual runoff volume to increase in the upper Hutuo River Basin.